A common way to avoid the need for a large labeled set for a dense prediction task such as semantic segmentation, is to annotate a small set of examples and use a semi-supervised learning approach for the remaining unlabeled data to train a model. Semi-supervised semantic segmentation has been widely studied using different mechanisms. Some methods utilize GAN-based adversarial learning [1]}, [2]}, while others [3]}, [4]} generate pseudo supervision from the trained model (e.g.self-training).
Consistency regularization can be used to address Semi-supervised semantic segmentation [5]}, [6]}, [7]}. This is done by enforcing a learned model to produce robust predictions for perturbations of unlabeled examples. Consistency regularization for semantic segmentation was first successfully used in a medical imaging application and has since been applied to other domains. Olsson [7]} propose a similar technique along this line by using predictions of a segmentation network to construct mixing, thus encouraging consistency over highly varied mixed samples while respecting the semantic boundaries of the original images. Our proposed method is inspired by [7]}, [10]} to make use of ideas from domain adaptation as well as semi-supervised learning.
